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Rename guess -> value

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Keep it consistent with lmfit naming convention
This commit is contained in:
usov_i 2021-02-24 15:44:23 +01:00
parent 55f3198f9d
commit 9e3466fcaa
3 changed files with 26 additions and 26 deletions
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10
pyzebra/app/panel_ccl_integrate.py
View File

@ -410,7 +410,7 @@ def create():
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, default_size=145)
fitparams_select.tags = [0]
@ -446,17 +446,17 @@ def create():
n = len(params)
fitparams = dict(
param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
)
return fitparams
fitparams_table_source = ColumnDataSource(dict(param=[], guess=[], vary=[], min=[], max=[]))
fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="guess", title="Guess", editor=NumberEditor()),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
@ -507,7 +507,7 @@ def create():
def _get_fit_params():
return dict(
guess=fit_params["gauss-1"]["guess"] + fit_params["background-0"]["guess"],
value=fit_params["gauss-1"]["value"] + fit_params["background-0"]["value"],
vary=fit_params["gauss-1"]["vary"] + fit_params["background-0"]["vary"],
constraints_min=fit_params["gauss-1"]["min"] + fit_params["background-0"]["min"],
constraints_max=fit_params["gauss-1"]["max"] + fit_params["background-0"]["max"],

10
pyzebra/app/panel_param_study.py
View File

@ -491,7 +491,7 @@ def create():
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, default_size=145)
fitparams_select.tags = [0]
@ -527,17 +527,17 @@ def create():
n = len(params)
fitparams = dict(
param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
)
return fitparams
fitparams_table_source = ColumnDataSource(dict(param=[], guess=[], vary=[], min=[], max=[]))
fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="guess", title="Guess", editor=NumberEditor()),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
@ -588,7 +588,7 @@ def create():
def _get_fit_params():
return dict(
guess=fit_params["gauss-1"]["guess"] + fit_params["background-0"]["guess"],
value=fit_params["gauss-1"]["value"] + fit_params["background-0"]["value"],
vary=fit_params["gauss-1"]["vary"] + fit_params["background-0"]["vary"],
constraints_min=fit_params["gauss-1"]["min"] + fit_params["background-0"]["min"],
constraints_max=fit_params["gauss-1"]["max"] + fit_params["background-0"]["max"],

32
pyzebra/fit2.py
View File

@ -33,7 +33,7 @@ def create_uncertanities(y, y_err):
def fitccl(
scan,
guess,
value,
vary,
constraints_min,
constraints_max,
@ -90,21 +90,21 @@ def fitccl(
if len(scan["peak_indexes"]) == 0:
# Case for no peak, gaussian in centre, sigma as 20% of range
peak_index = find_nearest(x, np.mean(x))
guess[0] = centre if guess[0] is None else guess[0]
guess[1] = (x[-1] - x[0]) / 5 if guess[1] is None else guess[1]
guess[2] = 50 if guess[2] is None else guess[2]
guess[3] = 0 if guess[3] is None else guess[3]
guess[4] = np.mean(y) if guess[4] is None else guess[4]
value[0] = centre if value[0] is None else value[0]
value[1] = (x[-1] - x[0]) / 5 if value[1] is None else value[1]
value[2] = 50 if value[2] is None else value[2]
value[3] = 0 if value[3] is None else value[3]
value[4] = np.mean(y) if value[4] is None else value[4]
constraints_min[2] = 0
elif len(scan["peak_indexes"]) == 1:
# case for one peak, takse into account users guesses
peak_height = scan["peak_heights"]
guess[0] = centre if guess[0] is None else guess[0]
guess[1] = 0.1 if guess[1] is None else guess[1]
guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
guess[3] = 0 if guess[3] is None else guess[3]
guess[4] = np.median(x) if guess[4] is None else guess[4]
value[0] = centre if value[0] is None else value[0]
value[1] = 0.1 if value[1] is None else value[1]
value[2] = float(peak_height / 10) if value[2] is None else float(value[2])
value[3] = 0 if value[3] is None else value[3]
value[4] = np.median(x) if value[4] is None else value[4]
constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
@ -121,11 +121,11 @@ def fitccl(
mod = Model(gaussian) + Model(background)
params = Parameters()
params.add_many(
("g_cen", guess[0], bool(vary[0]), np.min(x), np.max(x), None, None),
("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None),
("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
("g_cen", value[0], bool(vary[0]), np.min(x), np.max(x), None, None),
("g_width", value[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
("g_amp", value[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
("slope", value[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None),
("intercept", value[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
)
# the weighted fit
weights = [np.abs(1 / val) if val != 0 else 1 for val in y_err]